Hey everyone, we’re Shaun and Raul - cofounders of Galini.

TL;DR

Galini guardrails filter harmful inputs and outputs based on company policies and industry regulations. We make it easy for companies to create, evaluate, deploy and monitor guardrails.

With Galini, product and engineering leaders enjoy peace of mind knowing their AI apps are compliant at run-time, and save $1-10M from avoiding an in-house build.

Ask: If you are building or using AI applications, let us take run-time compliance off your plate. Email founders@galini.ai or please refer us to others who may need this.

Watch our demo here

❌ The Problem:

Compliance and trust are major barriers to scaling AI, with one in two executives fearing reputational damage.

Ignoring the problem does not make it go away: A staggering 44% of organizations have reported negative consequences from using AI chatbots and assistants.

The cost of non-compliance is rising: Fines of 7% annual revenues (or 35M EUR) for non-compliance with EU AI Act; similar regulations are fast approaching in the US.

Companies are not ready: Only 6% feel ready to accommodate these changes, while 80% are committing 10%+ of their AI budgets to compliance.

✅ Use cases:

Most customer- or employee-facing AI applications require guardrails:

• Search: Block non-compliant queries and prevent exposing sensitive or proprietary information to users.
• Chatbots: Ensure responses are accurate and compliant with company policies.
• Assistants: Stop misleading advice in violation with company policies that could cause negative PR or legal risks.
• Agents: Control scope and prevent unauthorized actions or transactions.
• Recommendation engines: Filter outputs to avoid biased, irrelevant or offensive suggestions that damage brand trust.
• Decisioning systems: Ensure automated decisions (e.g., loan approvals, hiring) meet regulatory standards and prevent discriminatory outcomes.

No enterprise-grade guardrail solutions exist today. Companies incur $1-10M+ in costs annually and 6-12 months in delays building guardrails in-house.

🎯 The Solution: Galini to the rescue

Galini’s guardrails as-a-service enforces runtime compliance with four core modules:

1. Build custom guardrails based on your company’ policies

   

2. Evaluate your guardrails using Galini’s synthetic test generator and evaluation engine

   

3. Deploy into your app in seconds with our API

   

4. Monitor and improve performance by providing feedback to Galini’s agent

   

🙏 Our ask:

• Follow us on LinkedIn, and book a demo with our team
• Share this post! Do you know product and engineering leaders in regulated industries like finance, healthcare, education and public safety? Please share this post; we’ll save them 1000+ hours in dev costs and many sleepless nights!
• Learn more at Galini.ai Please reach out to founders@galini.ai

🤝 The team:

Shaun Ayrton (CEO) – A former McKinsey leader, Shaun drove $500M+ in revenue acceleration at leading global software and telecom clients. He witnessed firsthand how companies struggle to manage the risks of AI deployment.

Raul Zablah (CTO) - As a former senior staff engineer, Raul has built and scaled industry-leading enterprise platforms at Bridgewater, Morgan Stanley and Ridgeline and has published papers in the field. His specialization is SRE and making technology work securely and reliably at enterprise scale.

Shaun and Raul met on their first day at UPenn’s M&T program in 2011 and have been dear friends since.